Many municipalities, governmental units, and private businesses have assets located at a variety of locations, such as factories located in several cities across the country or around the world. For various reasons, it may be important to consider risks to these locations and allocation of resources among such facilities. Such geographically distributed facilities may be thought of based on the area that they cover (e.g., the Midwest region restaurants of a fast food chain) or based on the network that they define (e.g., the network of an electric power distribution company).
As a specific example, a company that supplies ground transportation provides tractor-trailers to multiple ports around a country for hauling imports and products from each port to an inland destination. There are numerous reasons to supply the ports with a certain number of transportation assets (e.g., tractor-trailers, cargo trailers, loading equipment, repair equipment) which changes over time. The assets supplied to each port may differ for any of a variety of reasons that may or may not remain constant. For example, if one set of ports are experiencing labor difficulties, there may be need to dynamically shift transportation assets to another port as cargo ships are diverted, and then shift them back as labor issues are resolved.
In another example, a utility company may attempt to anticipate the potential impact of inclement weather and gauge the appropriate response, limited by finite funds and/or resources. Because of this limitation, the utility company strives to identify areas of its network at the greatest risk to a variety of issues, such as trees felled by wind or snow. In this setting, the utility company attempts to determine the optimal mix of spare equipment (e.g., poles, wire, transformers) used to respond to the event, and appropriately distribute the spare equipment across a number of staging areas (perhaps 50 locations throughout a geographic service area). Similarly, the utility company strives to optimize limited funds for engaging third parties (e.g., tree service contractors) to perform preventative maintenance along thousands of miles of roadways within its service area.
As another example, a fast food restaurant chain may have several hundred locations around the country. The headquarters of the company must determine, based on a wide variety of factors, how much of each food item to supply to each restaurant.
Such determinations apply in a wide variety of situations. For instance, aid organizations (e.g., the Red Cross, FEMA) maintain stocks of various disaster relief items in various warehouses. When a major weather event such as a hurricane is forecast, it may be advantageous to move supplies from one warehouse (e.g., in an area not likely to be impacted) to another (e.g., closer to the area likely to be impacted). Counter-intuitively, in some situations it may also be important to move supplies away from an area likely to be impacted, particularly if there is a threat that the supplies will be compromised by the catastrophic event if left at their current location.
Consider the operations of a railroad or municipal transportation authority. Knowing where to store operating equipment and stage spare equipment (rail, railcars, electrical transformers, and the like) can be critical to reducing downtime in the event of a catastrophic event, such as the storm surge that impacted the New York Subway system as a result of Tropical Storm Sandy in 2012.
Similar modeling and planning can be used in other industries as well. The insurance industry may well seek to model the impact of catastrophic events on various insured properties. In that industry, multiple layers of insurers have often-overlapping coverage, all with limits (e.g., caps) and other constraints. Further, catastrophic events, even if randomly distributed, are sometimes bunched so that exposure seems unusually high. In addition, some catastrophic events tend not to be independent but instead are tied together, e.g., (a) a weather pattern breeds multiple cyclonic events during a single season; (b) a large earthquake is accompanied by a tsunami and numerous aftershocks; (c) a terrorist attack is not isolated but is planned as one of several coordinated attacks. Continuous geographic distribution of insured assets such as a rail system complicates planning in various ways, so interest in modeling is particularly great in the insurance industry.
Determining the geospatial locations and how to best to allocate resources (e.g., electrical wires or train rails) to geographically diverse assets has traditionally been accomplished as a combination of geocoding and operations research. Geocoding conventionally uses location information such as an address or latitude/longitude coordinates as a representation of each asset under consideration (e.g., each fast food restaurant). Operations research takes a number of factors, including the location information, as a means to optimize the allocation of assets.
However, not all assets are readily described or optimized in this manner. Railroads, utility transmission lines, gas and oil pipelines and the like are continuously distributed throughout their geographic range, and in any event often do not have conventional physical addresses corresponding to the locations of their component parts. Many variables, such as the value of infrastructure, are not intended to be “optimized,” but rather just allocated.
The New York City Subway system, for example, has some two dozen rail yards, in addition to more than 200 miles of track on its two dozen or so routes. Some of these rail yards have dozens of tracks, with all of the associated switching devices and controls. Thus, the amount of spare equipment needed nearby to restore operation to the yards after a catastrophic event may be orders of magnitude more for the yards than for the route segments of the system. However, unlike the food delivery requirements for a group of fast food restaurants, the distribution of resource needs for the New York Subway system are based on continuous (rather than discrete) geographical distributions.
Consider now an insurance perspective on an asset that has continuous geographic distribution, such as the New York Subway system. Using computerized models, underwriters seek to price risk based on the evaluation of the probability of loss for a particular location and property type as well as manage portfolios of risks according to the degree to which losses correlate from one location to another as part of the same catastrophe event. These probabilistic (stochastic) catastrophe models include, but are not limited to, earthquake, fire following earthquake, tropical cyclone (hurricanes, typhoons, and cyclones), extra-tropical cyclone (windstorm), storm-surge, river flooding, tornadoes, hailstorms, terrorism and other types of catastrophe events. These catastrophe models are built upon detailed geographical databases describing highly localized variations in hazard characteristics, as well as databases capturing property and casualty inventory, building stock, and insurance exposure information.
Modeling systems using these models allow catastrophe managers, analysts, underwriters and others in the insurance markets (and elsewhere) to capture risk exposure data, to analyze risk for individual accounts or portfolios, to monitor risk aggregates, and to set business strategy. Typical catastrophe modeling systems are built around a geographical model comprising exposure information for individual locations, specific bounded locations or areas. These locations or areas of interest are typically defined using for example, a street address, postal code boundaries, including ZIP codes, city (or other administrative) boundaries, electoral or census ward boundaries and similar bounded geopolitical subdivisions.
A drawback of using these types of mechanisms (e.g., postal boundaries, cities, municipalities, building IDs, or ZIP codes) to define locations or areas is that some portions of an asset may not have an address or representative geopolitical boundaries that can be used to meaningfully characterize their corresponding risk exposures. Indeed, some portions of the asset (e.g, train cars, locomotives, cargo) may themselves be moveable properties without a fixed location.
Another drawback of these types of mechanisms to define locations or areas is that they do not allow the system or user the flexibility to select different resolutions that would provide the better geospatial representations of the asset. In addition, it may be very difficult to identify a single location that characterizes the risk of the whole geographic area.
These and other drawbacks exist. For asset portions having a fixed location but not a corresponding conventional address, use of a proxy such as ZIP code may result in extremely poor asset allocations and modeling results. For asset portions that are moveable, modeling that assumes the asset to be at a single geographic location again may poorly represent the actual exposure for any particular catastrophic event.